• 한국노년학
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• v.29 no.4
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• pp.1341-1359
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• 2009

In the wake of the radical structural change of the society, problems in family support for the old aged have arisen. Against this backdrop, the aim of this study was to examine aspects of the conflicts in family support focusing on novels by Park Wan-seo, and to analyze the expression of the family support awareness within the social consciousness. It selected the works that contain the contents of the elderly's support among 92 short stories, which were published from 1970s and 2006. The short novels that are contained the elderly's support are 9; , , , , ,

• Korean Journal of Culture and Social Issue
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• v.16 no.3
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• pp.241-265
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• 2010

This study reviewed the present state and differences of rehabilitation programs of the gambling addiction by comparing with other psychiatric disorder(including psychotic disability and alcohol addiction). This study also intended to suggest necessities, meanings and inherent fields of the rehabilitation in gambling addiction. First of all, the government and a few gambling industries run clinic centers for gamblers and their families, but have been lacked rehabilitation services for social comeback and adaptation or devaluated rehabilitation services than therapies. Gambling addict didn't have impairments of the cognitive function and their daily abilities was better than any other psychiatric disorders. But Damage of social role or function of gambling addiction was severe. And it is caused by nonadaptive nature of gambling behavior, personality/emotional change through gambling addiction process, and previous personality problem etc. There are many severe failure of social role and its attendant bankrupcy, family's problems and social poverty in gambling addiction, Therefore, important fields in the rehabilitation of gambling addiction should be services for basic social comeback support service, credit recovery support, monetary management, support of rehabilitation of family and vocational rehabilitation. Finally, the significance and critical points of the current study has been discussed as well.

• Asia Marketing Journal
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• v.12 no.2
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• pp.111-133
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• 2010

Marketing financial services used to be easier. Today, the competition in financial services is fierce. Not only has the competition become more intense, financial services have also changed structurally. In an environment with various customer needs and severe competitions, the marketing in financial services industry is getting more difficult and more important than before. However, there are still not enough studies on financial services marketing in Korea whereas lots of research papers have been published frequently in some international journals. The purpose of this paper is (1)to review the literature on financial services marketing, (2)to investigate current marketing activities based on in-depth interview with financial marketing managers in Korea, and (3)to suggest some implications for future research on the financial services marketing. Financial products are not consumer products. In fact, they are not products at all in the way product marketing is usually described. Nor are they altogether like services. The financial industry operates in a unique way, and its marketing tasks are correspondingly complex. However, the literature review shows that there has been a lack of basic studies which dealt with inherent characteristics of financial services marketing compared to the research on marketing in other industries. Many studies in domestic marketing journals have so far focused only on the general customer behaviors and the special issues in some financial industries. However, for more effective financial services marketing, we have to answer following questions. Is there any difference between financial service marketing and consumer packaged goods marketing? What are the differences between the financial services marketing and other services marketing such as education and health services? Are there different ways of marketing among banks, securities firms, insurance firms, and credit card companies? In other words, we need more detailed research as well as basic studies about the financial services marketing. For example, we need concrete definitions of financial services marketing, bank marketing, securities firm marketing, and etc. It is also required to compare the characteristics of each marketing within the financial services industry. The products sold in each market have different characteristics such as duration and degree of risk-taking. It means that there are sub-categories in financial services marketing. We have to consider them in the future research on the financial services marketing. It is also necessary to study customer decision making process in the financial markets. There have been little research on how customers search and process information, compare alternatives, make final decision, and repeat their choices. Because financial services have some unique characteristics, we need different understandings in the customer behaviors compared to the behaviors in other service markets. And also considering the rapid growth in financial markets and upcoming severe competition between domestic and global financial companies, it is time to start more systematic and detailed research on financial services marketing in Korea. In the second part of this paper, I analyzed the results of in-depth interview with 20 marketing managers of financial services companies in Korea. As a result, I found that the role of marketing departments in Korean financial companies are mainly focused on the short-term activities such as sales support, promotion, and CRM data analysis although the size and history of marketing departments to some extent show a sign of maturity. Most companies established official marketing departments before 2001. Average number of employees in a marketing department is about 58. However, marketing managers in eight companies(40% of the sample) still think that the purpose of marketing is only to support and manage general sales activities. It shows that some companies have sales-oriented concept rather than marketing-oriented concept. I also found three key words which marketing managers think importantly in financial services markets. They are (1)Trust in customer relationship, (2)Brand differentiation, and (3)Rapid response to customer needs. 50% of the sample support that "Trust" is the most important key word in the financial services marketing. It is interesting that 80% of banks and securities companies think that "Trust" is the most important thing, whereas managers in credit card companies consider "Rapid response to customer needs" as the most important key word in their market. In addition, there are different problems recognition of marketing managers depending on the types of financial industries they belong to. For example, in the case of banks and insurance companies, marketing managers consider "a lack of communication with other departments" as the most serious problem. On the other hand, in the case of securities firms, "a lack of utilization of customer data" is the most serious problem. These results imply that there are different important factors for the customer satisfaction depending on the types of financial industries, and managers have to consider them when marketing financial products in more effective ways. For example, It will be necessary for marketing managers to study different important factors which affect customer satisfaction, repeat purchase, degree of risk-taking, and possibility of cross-selling according to the types of financial industries. I also suggested six hypothetical propositions for the future research.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• The Korean Journal of Air & Space Law and Policy
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• v.25 no.2
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• pp.163-199
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• 2010

A frequent flyer program is a loyalty program offered by many airlines. Typically, airline customers enrolled in the program accumulate frequent flyer miles corresponding to the distance flown on that airline or its partners. There are other ways to accumulate miles. In recent years, more miles were awarded for using co-branded credit and debit cards than for air travel. Acquired miles can be redeemed for free air travel; for other goods or services, such as travel class upgrades, airport lounge access or priority bookings. The first modern frequent flyer program was created Texas International Airlines in 1979. This program was also adopted in Korean Air in 1984. Since then, the mileage programs have grown enormously. As of June 2009, the total member of two national airlines in Korea had been over thirty million. However, accumulated miles could be burden of airlines, because the korean corporations should record the annual financial report the accumulate mileage on a liability account by 'the international financial report standards(IFRS)' next year. The korean airlines need to minimize the accumulated miles, so that for instance Korean Airlines SKYPASS-miles expire 5 years after being earned. It means that miles earned on or after July 2008 will expire after five years if unredeemed. Thus, this paper attempt to analyze the unfairness of the mileage rules of korean airlines by examining a specific portion of the conditions relating to consumer protection, because many mileage users has difficulties using mileage programs and complained the amendment of the mileage rules. In conclusion, the contemporary mileage rules in Korea are rather unsatisfactory, because airlines is not only recognizing a mileage into a kind of benefit but also denying inheritance of mileage and the legal nature of mileage as a property right. It is necessary to amend relevant mileage rules in view of consumer protection, because air mileage is not simple benefit but a right of mileage user.

• Survey Research
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• v.10 no.1
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• pp.1-32
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• 2009

This study examines the changes of income, everyday life and living condition of consumer in the aftermath of financial crisis. In this period financial crisis was the crucial factor behind various social problems such as the dissolution of families and individuals. This research explores the range and degree of impact on individuals and social groups after the financial crisis. We explore the social mobility in terns of maintaining middle class and falling into the lower class measured by middle class identification. The 60% of the middle class before the financial crisis maintained the middle class position and the rest of people left out of middle class and fell into lower class. The 60% of those who has been maintained and has just became the members of middle class were college - educated people. The great part of people whose income and assets has increased after financial crisis belongs to college - educated group. Many of those whose income have decreased belong to the high school educated group and blow, the older than 50 years old, self - employed without employee and unpaid family employee. Those whose income and assets decreased and those who experienced downward mobility have undergone changes in everyday life and living conditions as a consumer. Many of them experienced the unemployment, nonpayment or credit - delinquency, dissolution of family, worsening health condition, depression, feeling the impulse to commit suicide simultaneously. The poor consumer disposition, reduction of living expenses, sound consumer culture have expanded to people since economic crisis. The middle class reported that the cost of private education often goes beyond the family ability to pay. The lower class has suffered from the cost of living. In a meanwhile luxury goods preference, consumer consciousness for status symbol have continuously increased among all the classes since 1997. Thus fluctuations of one's income and social mobility during past 10 years were some of the major determinants which brought about the various damaging life events, changes of living conditions and everyday lives as a consumer.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Korean Journal of Agricultural Science
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• v.14 no.1
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• pp.164-185
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• 1987

The Korean economy has been developed rapidly in the course of implementing the five year economic development plans since 1962. Accordingly the industrial and employment structure have been changed from the traditional agriculture to modem industrial economy. In the course of implementing export oriented industrialization policies, rural farm economy has been encountered labour shortage owing to rural farm population drain to urban areas, rural wage hike and pressure on farm operation costs, and possibility of farm productivity decrease. To cope with the above problems the Korean government has supplied farm machinery such as power tillers, tractors, transplanters, binders, combines, dryers and etc. by means of the favorable credit support and subsidies. The main objectives of this study are to identify the investment effects of farm mechanization such as B/C and Internal Rate of Return by machinery and operation patterns, changes of labour requirement per 10a for rice culture since 1965, partial farm budget of rice with and without mechanization, and estimation labour input with full mechanization. To achieve the objectives Saemaeul farm mechanization groups, common ownership and operation, and farms with private ownership and operation were surveyed mainly in Nonsan granary area, Chungnam province. The results of this study are as follows 1. The national average of labor input per 10a of paddy has decreased from 150.1Hr in 1965 to 87.2Hr in 1985 which showes 42% decrease of labour inputs. On the other hand the hours of labour input in Nonsan area have also decreased from 150.1Hr to 92.8Hr, 38% of that in 1965, during the same periods. 2. The possible labor saving hours per 10a of Paddy was estimated at 60 hours by substituting machine power for labor forces in the works of plowing, puddling, transplanting, harvesting and threshing, transporting and drying The labor savings were derived from 92.8 hours in 1986 deducting 30 hours of labor input with full mechanization in Nonsan area. 3. Social benefits of farm mechanization were estimated at 124,734won/10a including increment of rice (10%): 34,064won,labour saving: 65,800won,savings of conventional farm implements: 18,000 won and savings of animal power: 6,870won. 4. Rental charges by works prevailing in the area were 12,000won for land preparation, 15,000won for transplanting with seedlings, 19,500won for combine works and 6,000won for drying paddy. 5. Farm income per 10a of paddy with and without mechanization were amounted to 247,278won and 224,768won respectively. 6. Social rate of return of the machinery were estimated at more than 50% in all operation patterns. On the other hand internal rate of return of the machinery except tractors were also more than 50% but IRR of tractors by operation patterns were equivalent to 0 to 9%. From the view point of farmers financial status, private owner-operation of tractors is considered uneconomical. Tractor operation by Saemaeul mechanization groups would be economical considering the government subsidy, 40% of tractor price. 7. Farmers recommendations for the government that gained through field operation of farm machinery are to train maintenance technology for rural youth, to standardize the necessary parts of machinery, to implement price tag system, to intercede spare parts and provide marketing information to farmers by rural institutions as RDA,NACF,GUN office and FLIA.

• KDI Journal of Economic Policy
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• v.12 no.2
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• pp.135-149
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• 1990

Housing owners in Korea have a variety of tax advantages such as income tax exemption for the imputed rent of owner-occupied housing, exemption from the capital gains tax and deduction of the estate tax for one-house households. These tax reliefs for housing owners not only conflict with the principle of horizontal and vertical equity, but also lead to resource misallocation by distorting the housing market, and thus bring about regressive distribution effects. Particularly in the case of Korea with its imperfect capital market, these measures exacerbate the inter-class inequality of housing ownership as well as inequalities in wealth, by causing the affluent to demand needlessly large housing, while the poor and young experience difficulties in purchasing residential properties. Therefore, the Korean tax system must be altered as follows in order to disadvantage owner-occupiers, especially those owners of luxury housing. These alterations will promote housing-ownership, tax burden equity, efficiency of resource allocation, as well as the desirable distribution of income. First, income tax deductions for the rent payments of tenants are recommended. Ideally, the way of recovering the fiscal equivalence between the owner-occupiers and tenants is to levy an income tax on the former's imputed rents, and if necessary to give them tax credits. This, however, would be very difficult from a practical viewpoint, because the general public may perceive the concept of "imputed rent" as cumbersome. Computing the imputed rent also entails administrative costs, rendering quite reasonable, the continued exemption of imputed rent from taxation with the simultaneous deduction in the income tax for tenants. This would further enhance the administrative efficiency of income tax collection by easing assessment of the landlord's income. Second, a capital gains tax should be levied on the one-house household, except with the postponement of payments in the case that the seller purchases higher priced property. Exemption of the capital gains tax for the one-house household favors those who have more expensive housing, providing an incentive to the rich to hold even larger residences, and to the constructors to build more luxurious housing to meet the demand. So it is not desirable to sustain the current one-house household exemption while merely supplementing it with fastidious measures. Rather, the rule must be abolished completely with the concurrent reform of the deduction system and lowering of the tax rate, measures which the author believes will help optimize the capital gains tax incidence. Finally, discontinuation of the housing exemption for the heir is suggested. Consequent increases in the tax burden of the middle class could be mitigated by a reduction in the rate. This applies to the following specific exemptions as well, namely, for farm lands, meadows, woods, business fields-to foster horizontal equity, while denying speculation on land that leads to a loss in allocative efficiency. Moreover, imperfections in the Korean capital market have disallowed the provision of long term credit for housing seekers. Remedying these problems is essential to the promotion of greater housing ownership by the low and middle income classes. It is also certain that a government subsidy be focused on the poorest of the poor who cannot afford even to think of owning a housing.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.